Integrated Genomic and Social Network Analyses of SARS-CoV-2 Transmission in the Healthcare Setting.

BACKGROUND: Infection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2. METHODS: We performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs. RESULTS: Among 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001). CONCLUSIONS: IP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission.

Longitudinal assessment of the impact of COVID-19 infection on mask-wearing behaviors.

BACKGROUND: Wearing a mask was a crucial component in slowing the COVID-19 pandemic. However, little is known about the intersectionality between mask usage, risk perception, and infection. The purpose of this study was to investigate whether risk perceptions and masking behaviors are associated with contracting SARS-CoV-2 and how contracting SARS-CoV-2 subsequently changes masking behaviors in specific situations. METHODS: This cohort study utilized survey data from the UC San Diego ZAP COVID-19 study (n = 1,230) to evaluate the risk of contracting SARS-CoV-2 in relation to baseline risk perceptions and masking behaviors in various situations and how contracting SARS-CoV-2 affects subsequent masking behavior. RESULTS: We found that more consistent self-reported mask use in indoor public spaces (p = 0.03) and in other people's houses (p = 0.002) was associated with remaining free of SARS-CoV-2 infection. We also found that contracting SARS-CoV-2 was associated with a subsequent increase in mask use in other people's houses (p = 0.01). CONCLUSIONS: Our findings suggest that consistent mask use is correlated with decreased infection and that contracting SARS-CoV-2 may modify mask use behaviors in high-risk situations. These findings may help inform future public health messaging for infectious disease prevention. TRIAL REGISTRATION: This study has not been previously registered as it is an observational study. There was no pre-registration of the analytic plan for the present study.

Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study.

BACKGROUND: Sepsis costs and incidence vary dramatically across diagnostic categories, warranting a customized approach for implementing predictive models. OBJECTIVE: The aim of this study was to optimize the parameters of a sepsis prediction model within distinct patient groups to minimize the excess cost of sepsis care and analyze the potential effect of factors contributing to end-user response to sepsis alerts on overall model utility. METHODS: We calculated the excess costs of sepsis to the Centers for Medicare and Medicaid Services (CMS) by comparing patients with and without a secondary sepsis diagnosis but with the same primary diagnosis and baseline comorbidities. We optimized the parameters of a sepsis prediction algorithm across different diagnostic categories to minimize these excess costs. At the optima, we evaluated diagnostic odds ratios and analyzed the impact of compliance factors such as noncompliance, treatment efficacy, and tolerance for false alarms on the net benefit of triggering sepsis alerts. RESULTS: Compliance factors significantly contributed to the net benefit of triggering a sepsis alert. However, a customized deployment policy can achieve a significantly higher diagnostic odds ratio and reduced costs of sepsis care. Implementing our optimization routine with powerful predictive models could result in US $4.6 billion in excess cost savings for CMS. CONCLUSIONS: We designed a framework for customizing sepsis alert protocols within different diagnostic categories to minimize excess costs and analyzed model performance as a function of false alarm tolerance and compliance with model recommendations. We provide a framework that CMS policymakers could use to recommend minimum adherence rates to the early recognition and appropriate care of sepsis that is sensitive to hospital department-level incidence rates and national excess costs. Customizing the implementation of clinical predictive models by accounting for various behavioral and economic factors may improve the practical benefit of predictive models.

Telehealth in the COVID-19 Era: A Balancing Act to Avoid Harm.

The telehealth revolution in response to COVID-19 has increased essential health care access during an unprecedented public health crisis. However, virtual patient care can also limit the patient-provider relationship, quality of examination, efficiency of health care delivery, and overall quality of care. As we witness the most rapidly adopted medical trend in modern history, clinicians are beginning to comprehend the many possibilities of telehealth, but its limitations also need to be understood. As outcomes are studied and federal regulations reconsidered, it is important to be precise in the virtual patient encounter approach. Herein, we offer some simple guidelines that could assist health care providers and clinic schedulers in determining the appropriateness of a telehealth visit by considering visit types, patient characteristics, and chief complaint or disease states.

The Impact of Inpatient Telemedicine on Personal Protective Equipment Savings During the COVID-19 Pandemic: Cross-sectional Study.

With the emergence of the COVID-19 pandemic and shortage of adequate personal protective equipment (PPE), hospitals implemented inpatient telemedicine measures to ensure operational readiness and a safe working environment for clinicians. The utility and sustainability of inpatient telemedicine initiatives need to be evaluated as the number of COVID-19 inpatients is expected to continue declining. In this viewpoint, we describe the use of a rapidly deployed inpatient telemedicine workflow at a large academic medical center and discuss the potential impact on PPE savings. In early 2020, videoconferencing software was installed on patient bedside iPads at two academic medical center teaching hospitals. An internal website allowed providers to initiate video calls with patients in any patient room with an activated iPad, including both COVID-19 and non-COVID-19 patients. Patients were encouraged to use telemedicine technology to connect with loved ones via native apps or videoconferencing software. We evaluated the use of telemedicine technology on patients' bedside iPads by monitoring traffic to the internal website. Between May 2020 and March 2021, there were a total of 1240 active users of the Video Visits website (mean 112.7, SD 49.0 connection events per month). Of these, 133 (10.7%) connections were made. Patients initiated 63 (47.4%) video calls with family or friends and sent 37 (27.8%) emails with videoconference connection instructions. Providers initiated a total of 33 (24.8%) video calls with the majority of calls initiated in August (n=22, 67%). There was a low level of adoption of inpatient telemedicine capability by providers and patients. With sufficient availability of PPE, inpatient providers did not find a frequent need to use the bedside telemedicine technology, despite a high census of patients with COVID-19. Compared to providers, patients used videoconferencing capabilities more frequently in September and October 2020. We did not find savings of PPE associated with the use of inpatient telemedicine.

Medical Undistancing Through Telemedicine: A Model Enabling Rapid Telemedicine Deployment in an Academic Health Center During the COVID-19 Pandemic.

Background: The authors draw upon their experience with a successful, enterprise-level, telemedicine program implementation to present a "How To" paradigm for other academic health centers that wish to rapidly deploy such a program in the setting of the COVID-19 pandemic. The advent of social distancing as essential for decreasing viral transmission has made it challenging to provide medical care. Telemedicine has the potential to medically undistance health care providers while maintaining the quality of care delivered and fulfilling the goal of social distancing. Methods: Rather than simply reporting enterprise telemedicine successes, the authors detail key telemedicine elements essential for rapid deployment of both an ambulatory and inpatient telemedicine solution. Such a deployment requires a multifaceted strategy: (1) determining the appropriateness of telemedicine use, (2) understanding the interface with the electronic health record, (3) knowing the equipment and resources needed, (4) developing a rapid rollout plan, (5) establishing a command center for post go-live support, (6) creating and disseminating reference materials and educational guides, (7) training clinicians, patients, and clinic schedulers, (8) considering billing and credentialing implications, (9) building a robust communications strategy, and (10) measuring key outcomes. Results: Initial results are reported, showing a telemedicine rate increase to 45.8% (58.6% video and telephone) in just the first week of rollout. Over a 5-month period, the enterprise has since conducted over 119,500 ambulatory telemedicine evaluations (a 1,000-fold rate increase from the pre-COVID-19 time period). Conclusion: This article is designed to offer a "How To" potential best practice approach for others wishing to quickly implement a telemedicine program during the COVID-19 pandemic.

The Compelling Need for Shared Responsibility of AI Oversight: Lessons From Health IT Certification.

This Viewpoint highlights the potential for artificial intelligence (AI) health care tools to introduce unintended patient harm; calls for an efficient, rigorous approach to AI testing and certification that is the shared responsibility of developers and users; and makes recommendations to inform such an approach.

Crowdsourcing Diagnosis for Patients With Undiagnosed Illnesses: An Evaluation of CrowdMed.

BACKGROUND: Despite visits to multiple physicians, many patients remain undiagnosed. A new online program, CrowdMed, aims to leverage the "wisdom of the crowd" by giving patients an opportunity to submit their cases and interact with case solvers to obtain diagnostic possibilities. OBJECTIVE: To describe CrowdMed and provide an independent assessment of its impact. METHODS: Patients submit their cases online to CrowdMed and case solvers sign up to help diagnose patients. Case solvers attempt to solve patients' diagnostic dilemmas and often have an interactive online discussion with patients, including an exchange of additional diagnostic details. At the end, patients receive detailed reports containing diagnostic suggestions to discuss with their physicians and fill out surveys about their outcomes. We independently analyzed data collected from cases between May 2013 and April 2015 to determine patient and case solver characteristics and case outcomes. RESULTS: During the study period, 397 cases were completed. These patients previously visited a median of 5 physicians, incurred a median of US $10,000 in medical expenses, spent a median of 50 hours researching their illnesses online, and had symptoms for a median of 2.6 years. During this period, 357 active case solvers participated, of which 37.9% (132/348) were male and 58.3% (208/357) worked or studied in the medical industry. About half (50.9%, 202/397) of patients were likely to recommend CrowdMed to a friend, 59.6% (233/391) reported that the process gave insights that led them closer to the correct diagnoses, 57% (52/92) reported estimated decreases in medical expenses, and 38% (29/77) reported estimated improvement in school or work productivity. CONCLUSIONS: Some patients with undiagnosed illnesses reported receiving helpful guidance from crowdsourcing their diagnoses during their difficult diagnostic journeys. However, further development and use of crowdsourcing methods to facilitate diagnosis requires long-term evaluation as well as validation to account for patients' ultimate correct diagnoses.

Optimizing care of adults with congenital heart disease in a pediatric cardiovascular ICU using electronic clinical decision support*.

OBJECTIVES: The optimal location for postoperative cardiac care of adults with congenital heart disease is controversial. Some congenital heart surgeons operate on these adults in children's hospitals with postoperative care provided by pediatric critical care teams who may be unfamiliar with adult national performance measures. This study tested the hypothesis that Clinical Decision Support tools integrated into the clinical workflow would facilitate improved compliance with The Joint Commission Surgical Care Improvement Project performance measures in adults recovering from cardiac surgery in a children's hospital. DESIGN: Retrospective chart review comparing compliance pre- and post-Clinical Decision Support intervention for Surgical Care Improvement Project measures addressed in the critical care unit: appropriate cessation of prophylactic antibiotics; controlled blood glucose; urinary catheter removal; and reinitiation of preoperative ß-blocker when indicated. SETTING: Cardiovascular ICU in a quaternary care freestanding children's hospital. PATIENTS: The cohort included 114 adults 18-70 years old recovering from cardiac surgery in our pediatric cardiovascular ICU. INTERVENTIONS: Clinical Decision Support tools including data-triggered alerts, smart documentation forms, and order sets with conditional logic were integrated into the workflow. MEASUREMENTS AND MAIN RESULTS: Compliance with antibiotic discontinuation was 100% pre- and postintervention. Compliance rates improved for glucose control (p = 0.007) and urinary catheter removal (p = 0.05). Documentation of ß-blocker therapy (nonexistent preintervention) was 100% postintervention. Composite compliance for all measures increased from 53% to 84% (p = 0.002). There were no complications related to institution of the Surgical Care Improvement Project measures. There was no in-hospital mortality. CONCLUSIONS: Compliance with the national adult postoperative performance measures can be excellent in a children's hospital with the help of Clinical Decision Support tools. This represents an important step toward providing high-quality care to a growing population of adults with congenital heart disease who may receive care in a pediatric center.

Impact of a virtual coaching program for women physicians on burnout, fulfillment, and self-valuation.

BACKGROUND: Coaching has been demonstrated to be an effective physician wellness intervention. However, this evidence-based intervention has not yet been widely adopted in the physician community. Documentation and implementation research of interventions to address physician burnout in real world settings is much needed. OBJECTIVE: Assess the impact of a virtual physician coaching program in women physicians. DESIGN: Pre- and post-intervention surveys administered to participants enrolled in the program (N = 329). Effect size was calculated comparing pre- and post-intervention paired data (N = 201). PARTICIPANTS: 201 women physicians from 40 states in the United States of America and 3 international participants. INTERVENTIONS: Participants were given access to an 8 week virtual coaching program including eight individual, six small group, and 24 large group sessions. MAIN MEASURES: Stanford Professional Fulfillment Inventory (PFI) containing categories for assessing professional fulfillment, burnout, and the Clinician Self-Valuation (SV) Scale (a measure of self-compassion). KEY RESULTS: Burnout was found in 77.1% (N = 155) of participants at baseline, which reduced to 33.3% (N = 67) at completion with large effect size (Cohen's d 1.11). The percentage of participants who endorsed significant professional fulfillment started at 27.4% (N = 55) and improved to 68.2% (N = 137) with a large effect size (Cohen's d 0.95). Self-valuation improved from 17.9% (N = 36) of the participants endorsing a compassionate self-improvement perspective to 64% of the same participants eight weeks later. The self-valuation metric showed a very large effect size (Cohen's d 1.28). CONCLUSIONS: Virtual physician coaching programs led by physician coaches can decrease burnout, improve professional fulfillment, and increase self-compassion. Non-institution-based opportunities for coaching available to any physician across the United States and internationally can facilitate access to effective physician well-being interventions.